Quasi-least squares regression
Author(s)
Bibliographic Information
Quasi-least squares regression
（Monographs on statistics and applied probability, 132）（A Chapman & Hall book）
CRC Press, c2014
Available at 12 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitudinal data, familial data, and data with multiple sources of correlation. In some settings, QLS also allows for improved analysis with an unstructured correlation matrix.
Special focus is given to goodness-of-fit analysis as well as new strategies for selecting the appropriate working correlation structure for QLS and GEE. A chapter on longitudinal binary data tackles recent issues raised in the statistical literature regarding the appropriateness of semi-parametric methods, such as GEE and QLS, for the analysis of binary data; this chapter includes a comparison with the first-order Markov maximum-likelihood (MARK1ML) approach for binary data.
Examples throughout the book demonstrate each topic of discussion. In particular, a fully worked out example leads readers from model building and interpretation to the planning stages for a future study (including sample size calculations). The code provided enables readers to replicate many of the examples in Stata, often with corresponding R, SAS, or MATLAB (R) code offered in the text or on the book's website.
Table of Contents
INTRODUCTION: Introduction. Review of Generalized Linear Models. QUASI-LEAST SQUARES THEORY AND APPLICATIONS: History and Theory of QLS Regression. Mixed Linear Structures and Familial Data. Correlation Structures for Clustered and Longitudinal Data. Analysis of Data with Multiple Sources of Correlation. Correlated Binary Data. Assessing Goodness of Fit and Choice of Correlation Structure for QLS and GEE. Sample Size and Demonstration. Bibliography. Index.
by "Nielsen BookData"